Tag Archives: human-robot interaction
According to some scientists, humans really do have a sixth sense. There’s nothing supernatural about it: the sense of proprioception tells you about the relative positions of your limbs and the rest of your body. Close your eyes, block out all sound, and you can still use this internal “map” of your external body to locate your muscles and body parts – you have an innate sense of the distances between them, and the perception of how they’re moving, above and beyond your sense of touch.
This sense is invaluable for allowing us to coordinate our movements. In humans, the brain integrates senses including touch, heat, and the tension in muscle spindles to allow us to build up this map.
Replicating this complex sense has posed a great challenge for roboticists. We can imagine simulating the sense of sight with cameras, sound with microphones, or touch with pressure-pads. Robots with chemical sensors could be far more accurate than us in smell and taste, but building in proprioception, the robot’s sense of itself and its body, is far more difficult, and is a large part of why humanoid robots are so tricky to get right.
Simultaneous localization and mapping (SLAM) software allows robots to use their own senses to build up a picture of their surroundings and environment, but they’d need a keen sense of the position of their own bodies to interact with it. If something unexpected happens, or in dark environments where primary senses are not available, robots can struggle to keep track of their own position and orientation. For human-robot interaction, wearable robotics, and delicate applications like surgery, tiny differences can be extremely important.
In the case of hard robotics, this is generally solved by using a series of strain and pressure sensors in each joint, which allow the robot to determine how its limbs are positioned. That works fine for rigid robots with a limited number of joints, but for softer, more flexible robots, this information is limited. Roboticists are faced with a dilemma: a vast, complex array of sensors for every degree of freedom in the robot’s movement, or limited skill in proprioception?
New techniques, often involving new arrays of sensory material and machine-learning algorithms to fill in the gaps, are starting to tackle this problem. Take the work of Thomas George Thuruthel and colleagues in Pisa and San Diego, who draw inspiration from the proprioception of humans. In a new paper in Science Robotics, they describe the use of soft sensors distributed through a robotic finger at random. This placement is much like the constant adaptation of sensors in humans and animals, rather than relying on feedback from a limited number of positions.
The sensors allow the soft robot to react to touch and pressure in many different locations, forming a map of itself as it contorts into complicated positions. The machine-learning algorithm serves to interpret the signals from the randomly-distributed sensors: as the finger moves around, it’s observed by a motion capture system. After training the robot’s neural network, it can associate the feedback from the sensors with the position of the finger detected in the motion-capture system, which can then be discarded. The robot observes its own motions to understand the shapes that its soft body can take, and translate them into the language of these soft sensors.
“The advantages of our approach are the ability to predict complex motions and forces that the soft robot experiences (which is difficult with traditional methods) and the fact that it can be applied to multiple types of actuators and sensors,” said Michael Tolley of the University of California San Diego. “Our method also includes redundant sensors, which improves the overall robustness of our predictions.”
The use of machine learning lets the roboticists come up with a reliable model for this complex, non-linear system of motions for the actuators, something difficult to do by directly calculating the expected motion of the soft-bot. It also resembles the human system of proprioception, built on redundant sensors that change and shift in position as we age.
In Search of a Perfect Arm
Another approach to training robots in using their bodies comes from Robert Kwiatkowski and Hod Lipson of Columbia University in New York. In their paper “Task-agnostic self-modeling machines,” also recently published in Science Robotics, they describe a new type of robotic arm.
Robotic arms and hands are getting increasingly dexterous, but training them to grasp a large array of objects and perform many different tasks can be an arduous process. It’s also an extremely valuable skill to get right: Amazon is highly interested in the perfect robot arm. Google hooked together an array of over a dozen robot arms so that they could share information about grasping new objects, in part to cut down on training time.
Individually training a robot arm to perform every individual task takes time and reduces the adaptability of your robot: either you need an ML algorithm with a huge dataset of experiences, or, even worse, you need to hard-code thousands of different motions. Kwiatkowski and Lipson attempt to overcome this by developing a robotic system that has a “strong sense of self”: a model of its own size, shape, and motions.
They do this using deep machine learning. The robot begins with no prior knowledge of its own shape or the underlying physics of its motion. It then repeats a series of a thousand random trajectories, recording the motion of its arm. Kwiatkowski and Lipson compare this to a baby in the first year of life observing the motions of its own hands and limbs, fascinated by picking up and manipulating objects.
Again, once the robot has trained itself to interpret these signals and build up a robust model of its own body, it’s ready for the next stage. Using that deep-learning algorithm, the researchers then ask the robot to design strategies to accomplish simple pick-up and place and handwriting tasks. Rather than laboriously and narrowly training itself for each individual task, limiting its abilities to a very narrow set of circumstances, the robot can now strategize how to use its arm for a much wider range of situations, with no additional task-specific training.
In a further experiment, the researchers replaced part of the arm with a “deformed” component, intended to simulate what might happen if the robot was damaged. The robot can then detect that something’s up and “reconfigure” itself, reconstructing its self-model by going through the training exercises once again; it was then able to perform the same tasks with only a small reduction in accuracy.
Machine learning techniques are opening up the field of robotics in ways we’ve never seen before. Combining them with our understanding of how humans and other animals are able to sense and interact with the world around us is bringing robotics closer and closer to becoming truly flexible and adaptable, and, eventually, omnipresent.
But before they can get out and shape the world, as these studies show, they will need to understand themselves.
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Every year, for just a few days in a major city, a small team of roboticists get to live the dream: ordering around their own personal robot butlers. In carefully-constructed replicas of a restaurant scene or a domestic setting, these robots perform any number of simple algorithmic tasks. “Get the can of beans from the shelf. Greet the visitors to the museum. Help the humans with their shopping. Serve the customers at the restaurant.”
This is Robocup @ Home, the annual tournament where teams of roboticists put their autonomous service robots to the test for practical domestic applications. The tasks seem simple and mundane, but considering the technology required reveals that they’re really not.
The Robot Butler Contest
Say you want a robot to fetch items in the supermarket. In a crowded, noisy environment, the robot must understand your commands, ask for clarification, and map out and navigate an unfamiliar environment, avoiding obstacles and people as it does so. Then it must recognize the product you requested, perhaps in a cluttered environment, perhaps in an unfamiliar orientation. It has to grasp that product appropriately—recall that there are entire multi-million-dollar competitions just dedicated to developing robots that can grasp a range of objects—and then return it to you.
It’s a job so simple that a child could do it—and so complex that teams of smart roboticists can spend weeks programming and engineering, and still end up struggling to complete simplified versions of this task. Of course, the child has the advantage of millions of years of evolutionary research and development, while the first robots that could even begin these tasks were only developed in the 1970s.
Even bearing this in mind, Robocup @ Home can feel like a place where futurist expectations come crashing into technologist reality. You dream of a smooth-voiced, sardonic JARVIS who’s already made your favorite dinner when you come home late from work; you end up shouting “remember the biscuits” at a baffled, ungainly droid in aisle five.
Caring for the Elderly
Famously, Japan is one of the most robo-enthusiastic nations in the world; they are the nation that stunned us all with ASIMO in 2000, and several studies have been conducted into the phenomenon. It’s no surprise, then, that humanoid robotics should be seriously considered as a solution to the crisis of the aging population. The Japanese government, as part of its robots strategy, has already invested $44 million in their development.
Toyota’s Human Support Robot (HSR-2) is a simple but programmable robot with a single arm; it can be remote-controlled to pick up objects and can monitor patients. HSR-2 has become the default robot for use in Robocup @ Home tournaments, at least in tasks that involve manipulating objects.
Alongside this, Toyota is working on exoskeletons to assist people in walking after strokes. It may surprise you to learn that nurses suffer back injuries more than any other occupation, at roughly three times the rate of construction workers, due to the day-to-day work of lifting patients. Toyota has a Care Assist robot/exoskeleton designed to fix precisely this problem by helping care workers with the heavy lifting.
The Home of the Future
The enthusiasm for domestic robotics is easy to understand and, in fact, many startups already sell robots marketed as domestic helpers in some form or another. In general, though, they skirt the immensely complicated task of building a fully capable humanoid robot—a task that even Google’s skunk-works department gave up on, at least until recently.
It’s plain to see why: far more research and development is needed before these domestic robots could be used reliably and at a reasonable price. Consumers with expectations inflated by years of science fiction saturation might find themselves frustrated as the robots fail to perform basic tasks.
Instead, domestic robotics efforts fall into one of two categories. There are robots specialized to perform a domestic task, like iRobot’s Roomba, which stuck to vacuuming and became the most successful domestic robot of all time by far.
The tasks need not necessarily be simple, either: the impressive but expensive automated kitchen uses the world’s most dexterous hands to cook meals, providing it can recognize the ingredients. Other robots focus on human-robot interaction, like Jibo: they essentially package the abilities of a voice assistant like Siri, Cortana, or Alexa to respond to simple questions and perform online tasks in a friendly, dynamic robot exterior.
In this way, the future of domestic automation starts to look a lot more like smart homes than a robot or domestic servant. General robotics is difficult in the same way that general artificial intelligence is difficult; competing with humans, the great all-rounders, is a challenge. Getting superhuman performance at a more specific task, however, is feasible and won’t cost the earth.
Individual startups without the financial might of a Google or an Amazon can develop specialized robots, like Seven Dreamers’ laundry robot, and hope that one day it will form part of a network of autonomous robots that each have a role to play in the household.
The Smart Home has been a staple of futurist expectations for a long time, to the extent that movies featuring smart homes out of control are already a cliché. But critics of the smart home idea—and of the internet of things more generally—tend to focus on the idea that, more often than not, software just adds an additional layer of things that can break (NSFW), in exchange for minimal added convenience. A toaster that can short-circuit is bad enough, but a toaster that can refuse to serve you toast because its firmware is updating is something else entirely.
That’s before you even get into the security vulnerabilities, which are all the more important when devices are installed in your home and capable of interacting with them. The idea of a smart watch that lets you keep an eye on your children might sound like something a security-conscious parent would like: a smart watch that can be hacked to track children, listen in on their surroundings, and even fool them into thinking a call is coming from their parents is the stuff of nightmares.
Key to many of these problems is the lack of standardization for security protocols, and even the products themselves. The idea of dozens of startups each developing a highly-specialized piece of robotics to perform a single domestic task sounds great in theory, until you realize the potential hazards and pitfalls of getting dozens of incompatible devices to work together on the same system.
It seems inevitable that there are yet more layers of domestic drudgery that can be automated away, decades after the first generation of time-saving domestic devices like the dishwasher and vacuum cleaner became mainstream. With projected market values into the billions and trillions of dollars, there is no shortage of industry interest in ironing out these kinks. But, for now at least, the answer to the question: “Where’s my robot butler?” is that it is gradually, painstakingly learning how to sort through groceries.
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For Dr. Hiroshi Ishiguro, one of the most interesting things about androids is the changing questions they pose us, their creators, as they evolve. Does it, for example, do something to the concept of being human if a human-made creation starts telling you about what kind of boys ‘she’ likes?
If you want to know the answer to the boys question, you need to ask ERICA, one of Dr. Ishiguro’s advanced androids. Beneath her plastic skull and silicone skin, wires connect to AI software systems that bring her to life. Her ability to respond goes far beyond standard inquiries. Spend a little time with her, and the feeling of a distinct personality starts to emerge. From time to time, she works as a receptionist at Dr. Ishiguro and his team’s Osaka University labs. One of her android sisters is an actor who has starred in plays and a film.
ERICA’s ‘brother’ is an android version of Dr. Ishiguro himself, which has represented its creator at various events while the biological Ishiguro can remain in his offices in Japan. Microphones and cameras capture Ishiguro’s voice and face movements, which are relayed to the android. Apart from mimicking its creator, the Geminoid™ android is also capable of lifelike blinking, fidgeting, and breathing movements.
Say hello to relaxation
As technological development continues to accelerate, so do the possibilities for androids. From a position as receptionist, ERICA may well branch out into many other professions in the coming years. Companion for the elderly, comic book storyteller (an ancient profession in Japan), pop star, conversational foreign language partner, and newscaster are some of the roles and responsibilities Dr. Ishiguro sees androids taking on in the near future.
“Androids are not uncanny anymore. Most people adapt to interacting with Erica very quickly. Actually, I think that in interacting with androids, which are still different from us, we get a better appreciation of interacting with other cultures. In both cases, we are talking with someone who is different from us and learn to overcome those differences,” he says.
A lot has been written about how robots will take our jobs. Dr. Ishiguro believes these fears are blown somewhat out of proportion.
“Robots and androids will take over many simple jobs. Initially there might be some job-related issues, but new schemes, like for example a robot tax similar to the one described by Bill Gates, should help,” he says.
“Androids will make it possible for humans to relax and keep evolving. If we compare the time we spend studying now compared to 100 years ago, it has grown a lot. I think it needs to keep growing if we are to keep expanding our scientific and technological knowledge. In the future, we might end up spending 20 percent of our lifetime on work and 80 percent of the time on education and growing our skills.”
Android asks who you are
For Dr. Ishiguro, another aspect of robotics in general, and androids in particular, is how they question what it means to be human.
“Identity is a very difficult concept for humans sometimes. For example, I think clothes are part of our identity, in a way that is similar to our faces and bodies. We don’t change those from one day to the next, and that is why I have ten matching black outfits,” he says.
This link between physical appearance and perceived identity is one of the aspects Dr. Ishiguro is exploring. Another closely linked concept is the connection between body and feeling of self. The Ishiguro avatar was once giving a presentation in Austria. Its creator recalls how he felt distinctly like he was in Austria, even capable of feeling sensation of touch on his own body when people laid their hands on the android. If he was distracted, he felt almost ‘sucked’ back into his body in Japan.
“I am constantly thinking about my life in this way, and I believe that androids are a unique mirror that helps us formulate questions about why we are here and why we have been so successful. I do not necessarily think I have found the answers to these questions, so if you have, please share,” he says with a laugh.
His work and these questions, while extremely interesting on their own, become extra poignant when considering the predicted melding of mind and machine in the near future.
The ability to be present in several locations through avatars—virtual or robotic—raises many questions of both philosophical and practical nature. Then add the hypotheticals, like why send a human out onto the hostile surface of Mars if you could send a remote-controlled android, capable of relaying everything it sees, hears and feels?
The two ways of robotics will meet
Dr. Ishiguro sees the world of AI-human interaction as currently roughly split into two. One is the chat-bot approach that companies like Amazon, Microsoft, Google, and recently Apple, employ using stationary objects like speakers. Androids like ERICA represent another approach.
“It is about more than the form factor. I think that the android approach is generally more story-based. We are integrating new conversation features based on assumptions about the situation and running different scenarios that expand the android’s vocabulary and interactions. Another aspect we are working on is giving androids desire and intention. Like with people, androids should have desires and intentions in order for you to want to interact with them over time,” Dr. Ishiguro explains.
This could be said to be part of a wider trend for Japan, where many companies are developing human-like robots that often have some Internet of Things capabilities, making them able to handle some of the same tasks as an Amazon Echo. The difference in approach could be summed up in the words ‘assistant’ (Apple, Amazon, etc.) and ‘companion’ (Japan).
Dr. Ishiguro sees this as partly linked to how Japanese as a language—and market—is somewhat limited. This has a direct impact on viability and practicality of ‘pure’ voice recognition systems. At the same time, Japanese people have had greater exposure to positive images of robots, and have a different cultural / religious view of objects having a ‘soul’. However, it may also mean Japanese companies and android scientists are both stealing a lap on their western counterparts.
“If you speak to an Amazon Echo, that is not a natural way to interact for humans. This is part of why we are making human-like robot systems. The human brain is set up to recognize and interact with humans. So, it makes sense to focus on developing the body for the AI mind, as well as the AI. I believe that the final goal for both Japanese and other companies and scientists is to create human-like interaction. Technology has to adapt to us, because we cannot adapt fast enough to it, as it develops so quickly,” he says.
Banner image courtesy of Hiroshi Ishiguro Laboratories, ATR all rights reserved.
Dr. Ishiguro’s team has collaborated with partners and developed a number of android systems:
Geminoid™ HI-2 has been developed by Hiroshi Ishiguro Laboratories and Advanced Telecommunications Research Institute International (ATR).
Geminoid™ F has been developed by Osaka University and Hiroshi Ishiguro Laboratories, Advanced Telecommunications Research Institute International (ATR).
ERICA has been developed by ERATO ISHIGURO Symbiotic Human-Robot Interaction Project Continue reading